An image or a spectrum of a surface may be acquired by a computing device, which may be included in a mobile device in some examples. The computing device may extract a measured spectrum from the image and generate a corrected spectrum of the surface. In some examples, the corrected spectrum may be generated to compensate for ambient light influence. The corrected spectrum may be analyzed to provide a result, such as a diagnosis or a product recommendation. In some examples, the result is based, at least in part, on a comparison of the corrected spectrum to reference spectra. In some examples, the result is based, at least in part, on an inference of a machine learning model.
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2. The device of claim 1, wherein the comparison of the corrected spectrum with reference spectra of the reference spectra of the database includes using a regression analysis method comprising at least one of least mean squares, least squares, or total least squares.
This invention relates to a device for analyzing spectral data, particularly for comparing a corrected spectrum against reference spectra stored in a database. The device addresses the challenge of accurately identifying or characterizing a sample by improving the comparison process between measured spectral data and known reference spectra. The corrected spectrum, which may have been preprocessed to remove noise or other artifacts, is compared to reference spectra using a regression analysis method. The regression analysis method includes at least one of least mean squares, least squares, or total least squares to enhance the accuracy and reliability of the comparison. These statistical techniques help minimize errors and improve the fit between the corrected spectrum and the reference spectra, enabling more precise identification or quantification of the sample. The device leverages these mathematical methods to refine the spectral analysis, ensuring that the comparison is robust against variations in the data. This approach is particularly useful in fields such as chemistry, materials science, and environmental monitoring, where accurate spectral matching is critical for analysis and decision-making.
3. The device of claim 1, wherein the result includes a spectrum of the reference spectra having a closest fit to the corrected spectrum, a group of spectra of the reference spectra having the closest fit at different spectral ranges, or a combination thereof.
This invention relates to spectral analysis systems, specifically devices that compare an input spectrum to a database of reference spectra to identify the closest matches. The problem addressed is the need for accurate and flexible spectral matching, particularly when dealing with noisy or distorted input data. The device corrects the input spectrum to improve matching accuracy, then compares it to reference spectra to determine the best fit. The result can include either a single reference spectrum with the closest overall match, a group of reference spectra that best match different spectral ranges, or a combination of both. This allows for more nuanced analysis, especially when different parts of the spectrum may align with different references. The system is useful in applications like material identification, chemical analysis, or quality control, where precise spectral matching is critical. The flexibility in output format ensures the device can adapt to various analytical needs, whether requiring a single best match or a detailed breakdown of partial matches across the spectrum.
4. The device of claim 1, further comprising a user interface configured to receive a preference from a user, wherein the user interface is coupled to a recommendation engine configured to provide the product recommendation based on the user's preference and the result.
This invention relates to a device for generating product recommendations based on user preferences and analysis results. The device includes a data processing module that analyzes input data to generate a result, such as identifying relevant features or patterns. A recommendation engine then uses this result to generate product recommendations tailored to the user. The device further includes a user interface that allows the user to input preferences, which the recommendation engine incorporates alongside the analysis result to refine the recommendations. This ensures that the suggestions align with the user's specific needs or preferences, improving the relevance and personalization of the output. The system may be applied in e-commerce, content recommendation, or other domains where personalized suggestions are valuable. The invention addresses the challenge of providing accurate and user-specific recommendations by combining automated data analysis with direct user input, enhancing the decision-making process for both individuals and businesses.
5. The device of claim 4, wherein the user interface is further configured to prompt the user to move the device to a new region of the surface and wherein the camera is further configured to take a second image of the new region, wherein the processor is further configured to extract a second measured spectrum from the second image of the new region, analyze the second measured spectrum to compensate for ambient light influence from the second measured spectrum and generate a second corrected spectrum, compare the second corrected spectrum with the database to generate a second result, and provide at least one of a second product recommendation or a second diagnosis based, at least in part, on the comparison between the corrected spectrum and the database and a second comparison of the second corrected spectrum and the database.
This invention relates to a portable device for analyzing surfaces, such as skin or materials, to provide product recommendations or diagnostic information. The device includes a camera configured to capture images of a surface region, a processor that extracts a measured spectrum from the image, and a user interface. The processor analyzes the measured spectrum to compensate for ambient light influence, generating a corrected spectrum. This corrected spectrum is compared to a database to generate a result, which is used to provide a product recommendation or diagnosis. The user interface prompts the user to move the device to a new region of the surface, and the camera captures a second image of this new region. The processor extracts a second measured spectrum from this second image, compensates for ambient light influence to generate a second corrected spectrum, and compares this second corrected spectrum to the database. Based on this comparison, the device provides a second product recommendation or diagnosis. The system ensures accurate analysis by accounting for variations in ambient lighting across different surface regions.
7. The device of claim 1, wherein the database is stored in a cloud computing device in communication with the processor or a local memory included with the device in communication with the processor.
This invention relates to a system for managing and processing data using a database. The system includes a processor configured to execute instructions for accessing and manipulating data stored in a database. The database can be stored either in a cloud computing device that communicates with the processor or in a local memory integrated with the device that houses the processor. The system is designed to provide flexibility in data storage, allowing users to choose between cloud-based storage for scalability and remote access or local storage for faster access and enhanced security. The processor is capable of performing various operations on the data, such as querying, updating, and analyzing the information stored in the database. The system may also include additional components, such as input/output interfaces, to facilitate user interaction and data transfer. The invention aims to improve data management efficiency by offering multiple storage options while maintaining performance and reliability.
8. The device of claim 1, wherein the processor is configured to implement a machine learning model to provide at least one of the product recommendation or the diagnosis, wherein the machine learning model is based at least in part, on user feedback.
A system for product recommendation or medical diagnosis uses a machine learning model trained on user feedback to generate outputs. The system includes a processor that executes the model, which analyzes input data to produce recommendations or diagnostic results. The model is trained using feedback from users, allowing it to improve accuracy and relevance over time. The system may also include input interfaces for receiving user data, such as preferences or symptoms, and output interfaces for delivering recommendations or diagnoses. The machine learning model is designed to adapt based on historical feedback, ensuring personalized and accurate results. This approach enhances decision-making in both commercial and medical applications by leveraging data-driven insights. The system may further include additional components, such as data storage for user feedback and processing units for real-time analysis. The integration of user feedback into the model ensures continuous improvement, making the system more reliable and tailored to individual needs.
9. The device of claim 1, wherein the surface comprises skin cells, and wherein the product recommendation comprises a foundation that matches a skin tone of the skin cells.
This invention relates to a device for analyzing skin characteristics and recommending cosmetic products, particularly foundation, based on skin tone. The device captures an image of a user's skin, processes the image to determine the skin tone, and generates a product recommendation that matches the detected tone. The system includes an imaging module to acquire the skin image, an analysis module to extract color and texture features, and a recommendation engine that selects a foundation product from a database of available options. The recommendation engine may also consider additional factors such as lighting conditions, skin type, or user preferences to refine the match. The device may be integrated into a mobile application or a standalone hardware unit, allowing users to receive personalized foundation recommendations quickly and accurately. This technology addresses the challenge of finding the right foundation shade by automating the matching process, reducing guesswork, and improving user satisfaction with cosmetic purchases. The system may also include feedback mechanisms to refine recommendations over time based on user input or additional data.
10. The device of claim 1, wherein the surface comprises a wall, and wherein the product recommendation comprises at least a décor that is complementary to the wall.
This invention relates to a device that provides product recommendations based on visual analysis of a surface, such as a wall. The device includes a camera to capture an image of the surface and a processor to analyze the image and generate recommendations for products that complement the surface. The recommendations may include décor items, such as paint colors, wallpaper, or other decorative elements, that are visually compatible with the wall's existing appearance. The device may also consider factors like lighting conditions, texture, and color to ensure the recommended products enhance the overall aesthetic. The system may further include a display to present the recommendations to the user, allowing them to preview how the suggested products would look in their space. The invention aims to simplify the process of selecting complementary décor by automating the analysis and recommendation process, reducing the need for manual color matching or trial-and-error selection. The device may be integrated into a mobile application or a standalone hardware unit, providing flexibility in how users interact with the system. By leveraging image processing and machine learning, the invention helps users make informed decisions about décor choices that harmonize with their existing surfaces.
11. The device of claim 1, further comprising a display, wherein the display provides at least one of the image of the surface, the product recommendation, or the diagnosis.
This invention relates to a device for analyzing surfaces, such as skin, and providing recommendations or diagnoses based on the analysis. The device captures an image of the surface using an imaging system, processes the image to identify characteristics, and generates a product recommendation or diagnosis based on the identified characteristics. The device includes a display that shows at least one of the captured image, the product recommendation, or the diagnosis to the user. The imaging system may use techniques such as spectroscopy, imaging spectroscopy, or other optical methods to analyze the surface. The device may also include a processor that processes the captured image to extract relevant data, such as texture, color, or other surface properties. The product recommendation or diagnosis is generated based on the extracted data, which may involve comparing the data to a database of known conditions or product formulations. The display provides visual feedback to the user, allowing them to see the analysis results and make informed decisions. The device may be used in applications such as dermatology, cosmetics, or industrial surface analysis.
12. The device of claim 1, wherein the light source is a visible light source or an infrared (IR) flood illuminator.
This invention relates to a device for illuminating a target area, particularly for applications such as imaging, sensing, or surveillance. The device addresses the challenge of providing consistent and uniform illumination across a target area, which is critical for accurate imaging or detection. The device includes a light source that emits light to illuminate the target area, and a control system that adjusts the light source's output to maintain uniform illumination despite variations in environmental conditions or target characteristics. The light source can be either a visible light source or an infrared (IR) flood illuminator, depending on the application requirements. Visible light sources are used when human visibility or color imaging is needed, while IR flood illuminators are preferred for low-light or nighttime applications where infrared imaging is employed. The device may also include additional components such as lenses, reflectors, or diffusers to shape and direct the light output for optimal coverage of the target area. The control system dynamically adjusts the light source's intensity, wavelength, or beam pattern to compensate for factors like ambient light changes, target reflectivity, or distance variations, ensuring consistent illumination performance. This adaptability enhances the reliability of imaging or sensing systems that rely on the device for accurate data acquisition.
13. The device of claim 1, wherein the device is a mobile device.
A mobile device is disclosed for capturing and processing images to enhance visual content. The device includes an image sensor configured to capture an image of a scene, a processor, and a memory storing instructions executable by the processor. The instructions cause the processor to analyze the captured image to detect one or more visual elements, such as objects, textures, or lighting conditions. The device then applies an enhancement algorithm to modify the detected visual elements, improving aspects like contrast, sharpness, or color balance. The enhanced image is then displayed or stored. The mobile device may further include a user interface allowing manual adjustments to the enhancement parameters. The enhancement algorithm may adapt dynamically based on environmental factors, such as ambient lighting or motion detected by the device's sensors. The device may also synchronize with external systems, such as cloud servers, to access additional processing resources or reference data for further image refinement. The mobile device's compact form factor and integrated components enable real-time image processing without requiring external hardware. This technology addresses the need for portable, high-quality image enhancement in consumer electronics, particularly in scenarios where immediate visual feedback is desired.
15. The method of claim 14, wherein extracting comprises decoding the color with red-green-blue (RGB) weighting; and providing an RGB color code of the color of the surface.
Colorimetric analysis and color data processing. This invention addresses the need for accurately capturing and representing surface colors. The method involves a process where color information is extracted from a surface. This extraction specifically includes a step of decoding the color by applying red-green-blue (RGB) weighting. Following this decoding and weighting, the process results in the provision of an RGB color code that represents the identified color of the surface. This allows for a standardized and digital representation of the observed color.
16. The method of claim 14, wherein extracting comprises analyzing the color spectrum further comprises applying MPEG7 global descriptors.
This invention relates to video processing, specifically methods for analyzing and extracting color spectrum data from video frames to improve content-based indexing and retrieval. The problem addressed is the need for efficient and accurate color-based analysis in video processing systems, which is crucial for applications like video search, content recommendation, and automated metadata generation. The method involves extracting color spectrum data from video frames by analyzing the color distribution within each frame. This analysis includes applying MPEG7 global descriptors, which are standardized features used to describe the overall color characteristics of an image or video segment. MPEG7 descriptors provide a compact and scalable representation of color information, enabling efficient comparison and retrieval of video content based on color similarity. The extracted color spectrum data is then used to generate metadata or perform content-based operations, such as identifying similar video segments, filtering content, or enhancing search capabilities. The use of MPEG7 descriptors ensures compatibility with existing multimedia standards and facilitates interoperability across different video processing systems. This approach improves the accuracy and efficiency of color-based video analysis, making it suitable for large-scale video databases and real-time applications.
17. The method of claim 14, wherein correcting the measured spectrum further comprises optimizing a signal to noise ratio of the measured spectrum.
This invention relates to spectral analysis, specifically improving the accuracy of measured spectra by correcting distortions and optimizing signal-to-noise ratio (SNR). The method addresses challenges in spectral measurements where noise, interference, or instrumental artifacts degrade data quality, making it difficult to extract meaningful information. The technique involves analyzing a measured spectrum to identify and correct distortions caused by factors such as detector nonlinearity, environmental interference, or optical aberrations. By applying mathematical models or calibration data, the method adjusts the spectrum to remove these distortions, enhancing its fidelity. Additionally, the method includes optimizing the SNR of the corrected spectrum, which may involve filtering, averaging, or adaptive noise reduction techniques to improve the clarity of the spectral features. The corrected and optimized spectrum provides more accurate and reliable data for applications in chemistry, materials science, or biomedical analysis. The approach is particularly useful in fields where high-precision spectral measurements are critical, such as drug discovery, environmental monitoring, or quality control in manufacturing. By systematically addressing both distortion correction and SNR optimization, the method ensures that the final spectrum is both accurate and robust against noise.
18. The method of claim 14, further comprising analyzing the corrected spectrum to determine a shininess of the surface, wherein the shininess of the surface is determined by a ratio of an intensity of a reflected beam and an intensity of an unpolarized light, and wherein the result is based, at least in part, on the shininess of the surface.
This invention relates to optical measurement techniques for analyzing surfaces, particularly for determining surface properties such as shininess. The problem addressed is the need for accurate and efficient surface characterization, which is crucial in industries like manufacturing, quality control, and material science. The method involves capturing a spectrum of light reflected from a surface and correcting it to account for distortions caused by the surface's optical properties. The corrected spectrum is then analyzed to determine the surface's shininess, which is calculated as the ratio of the intensity of a reflected beam to the intensity of unpolarized light. The shininess value is used to refine the analysis results, ensuring higher accuracy in surface characterization. This approach improves upon existing methods by providing a more precise and automated way to assess surface reflectivity, which is essential for applications requiring high-quality surface inspections. The technique can be integrated into optical measurement systems to enhance their performance in evaluating surface conditions.
19. The method of claim 14, further comprising analyzing at least one of the corrected spectrum or the spectrum having the closest fit with a machine learning model to make an inference; and providing the result based, at least in part, on the inference.
This invention relates to spectral analysis systems that use machine learning to improve data accuracy and inference capabilities. The method involves correcting a measured spectrum by comparing it to a reference spectrum and adjusting the measured spectrum to minimize differences, such as through scaling or shifting. The corrected spectrum is then analyzed using a machine learning model to generate an inference, such as identifying chemical compounds, material properties, or other spectral features. The system may also select a reference spectrum that best matches the measured spectrum before correction, ensuring higher accuracy in the analysis. The machine learning model processes the corrected or best-fit spectrum to produce a result, which could be a classification, prediction, or other analytical output. This approach enhances the reliability of spectral data interpretation by combining spectral correction techniques with machine learning-based inference. The method is applicable in fields like chemistry, materials science, and environmental monitoring, where precise spectral analysis is critical.
20. The method of claim 14, wherein comparing the corrected spectrum to the reference spectra includes comparing the corrected spectrum and the reference spectra across a range comprising an ultraviolet (UV) spectrum, a visible spectrum, and an infrared spectrum.
This invention relates to spectral analysis techniques for comparing corrected spectra against reference spectra across a broad wavelength range. The method addresses challenges in accurately identifying or characterizing materials when spectral data is influenced by environmental or instrumental factors. The process involves correcting a measured spectrum to account for distortions or noise, then comparing the corrected spectrum to a set of reference spectra. The comparison is performed across a wide spectral range, including ultraviolet (UV), visible, and infrared (IR) regions, to enhance detection accuracy and reliability. By analyzing multiple spectral bands simultaneously, the method improves the ability to distinguish between similar materials or identify subtle spectral features that may not be apparent in a single wavelength range. The technique is particularly useful in applications such as material identification, quality control, and environmental monitoring, where precise spectral matching is critical. The method ensures that the corrected spectrum is accurately aligned with the reference spectra, reducing false positives or negatives in the analysis. The inclusion of UV, visible, and IR spectra provides a comprehensive assessment, leveraging the unique absorption or emission characteristics of different materials across these regions. This approach enhances the robustness and versatility of spectral analysis systems.
21. The method of claim 14, wherein the result includes at least one of a product recommendation or a diagnosis.
This invention relates to a system for generating recommendations or diagnoses based on user input. The system processes input data, such as user preferences, historical behavior, or environmental conditions, to produce actionable outputs. The method involves collecting and analyzing data, applying machine learning or rule-based algorithms to derive insights, and presenting the results in a user-friendly format. The results may include product recommendations tailored to individual needs or diagnostic assessments for troubleshooting issues. The system may integrate with external databases or APIs to enhance accuracy and relevance. The method ensures real-time or batch processing, depending on the application, and supports customization for different industries, such as e-commerce, healthcare, or industrial maintenance. The invention aims to improve decision-making efficiency by automating the analysis of complex data sets and providing clear, actionable recommendations or diagnoses.
22. The method of claim 21, wherein the product recommendation is for a product having a spectrum complementary to the spectrum having the closest fit to the corrected spectrum, and wherein the product recommendation is based on a database of products based at least in part, on user feedback.
This invention relates to a method for generating product recommendations based on spectral analysis and user feedback. The method addresses the challenge of accurately matching products to user preferences by leveraging spectral data and refining recommendations through user input. The method involves analyzing a corrected spectrum of a user's environment or preferences, then identifying a product with a spectrum that is complementary to the spectrum with the closest fit to the corrected spectrum. Complementary spectra are those that enhance or balance the original spectrum, such as in color matching, sound optimization, or other spectral-based applications. The recommendation is generated from a database of products, where the database is populated and refined based on user feedback. This feedback loop ensures that the recommendations improve over time, adapting to user preferences and behaviors. The method may also include steps for correcting the initial spectrum, such as adjusting for environmental factors or user-specific adjustments, to ensure the most accurate product match. The database of products includes spectral data for each product, allowing for precise comparisons and recommendations. By combining spectral analysis with user feedback, the method provides personalized and adaptive product recommendations that evolve with user interactions.
25. The device of claim 24, further comprising a display configured to provide at least one of the image of the surface, the spectrum of the surface, and at least one of the product recommendation or the diagnosis.
This invention relates to a device for analyzing surfaces, particularly for diagnostic or product recommendation purposes. The device includes a sensor system capable of capturing an image of the surface and obtaining spectral data from the surface. The spectral data may include information about the surface's composition, condition, or other properties. The device further includes a processing unit that analyzes the captured image and spectral data to generate insights about the surface. These insights may include identifying surface defects, determining surface conditions, or assessing material properties. The device also includes a display configured to present the captured image, the spectral data, or derived information such as product recommendations or diagnostic results. The recommendations or diagnoses are based on the analysis of the surface's image and spectral characteristics. The device may be used in various applications, such as industrial quality control, medical diagnostics, or consumer product recommendations, where surface analysis is critical for decision-making. The display ensures that users can easily interpret the results, whether for maintenance, treatment, or product selection.
26. The device of claim 24, wherein the processor is further configured to determine a specular reflectance of the illuminated surface based at least in part on an intensity of a reflected beam and an intensity of an unpolarized light.
This invention relates to optical measurement systems for analyzing surface properties, specifically determining specular reflectance of illuminated surfaces. The problem addressed is accurately measuring specular reflectance, which is crucial for applications in material characterization, quality control, and surface inspection. Traditional methods often struggle with distinguishing between specular and diffuse reflections, leading to measurement inaccuracies. The device includes a light source that illuminates a surface with polarized light and a detector that measures the intensity of the reflected beam. A processor analyzes these measurements to calculate the specular reflectance. The processor compares the intensity of the reflected beam with the intensity of unpolarized light to isolate the specular component. This comparison helps eliminate interference from diffuse reflections, improving measurement precision. The system may also include polarization components to enhance the accuracy of the reflectance determination. The invention is particularly useful in industrial settings where surface quality and reflectivity are critical, such as in manufacturing, automotive coatings, and semiconductor inspection. By providing a more accurate and reliable method for specular reflectance measurement, the device enables better control over surface treatment processes and quality assurance. The system's ability to distinguish between different types of reflections ensures that the measurements are not skewed by environmental or material variations.
27. The device of claim 26, wherein the surface is determined to be shiny if the intensity of the reflected beam is greater than the intensity of the unpolarized light.
A system for detecting shiny surfaces analyzes reflected light to determine surface properties. The system emits unpolarized light onto a surface and measures the intensity of the reflected beam. The surface is classified as shiny if the intensity of the reflected beam exceeds the intensity of the incident unpolarized light. This detection method leverages the principle that shiny surfaces reflect light more strongly, increasing the reflected beam's intensity compared to the original unpolarized light. The system may include a light source, a sensor to measure reflected light intensity, and a processor to compare the reflected intensity with the unpolarized light intensity. The processor determines the surface's shininess based on this comparison. This approach is useful in applications requiring surface quality assessment, such as manufacturing, quality control, or material analysis, where distinguishing shiny from non-shiny surfaces is critical. The system may also incorporate additional features, such as polarization analysis or multi-angle reflection measurements, to enhance accuracy. The core innovation lies in the intensity-based comparison to classify surfaces as shiny, providing a simple yet effective method for surface characterization.
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August 27, 2020
May 28, 2024
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